mlsanityData to 7 Oct 2026

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

arXiv 2411.05536 ↗First posted 8 Nov 2024cs.LG physics.flu-dyncited by 1

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Abstract (arXiv)

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at Re = 100, the DRL approach achieved a 9.32% drag reduction and a 78.4% decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 8 Nov 2024.

Builds on

3 of 18 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modelingcited by 7
  3. 2023Active flow control for three-dimensional cylinders through deep reinforcement learningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costscited by 1